EP2384488B1 - Electronic learning synapse with spike-timing dependent plasticity using memory-switching elements - Google Patents
Electronic learning synapse with spike-timing dependent plasticity using memory-switching elements Download PDFInfo
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- EP2384488B1 EP2384488B1 EP10717576.2A EP10717576A EP2384488B1 EP 2384488 B1 EP2384488 B1 EP 2384488B1 EP 10717576 A EP10717576 A EP 10717576A EP 2384488 B1 EP2384488 B1 EP 2384488B1
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Definitions
- the present invention relates to artificial neural networks, and more specifically, to electronic learning synapses with spike-dependent plasticity.
- the point of contact between an axon of a neuron and a dendrite on another neuron is called a synapse, and with respect to the synapse, the two neurons are respectively called pre-synaptic and post-synaptic.
- the essence of our individual experiences is stored in conductance of the synapses.
- the synaptic conductance changes with time as a function of the relative spike times of pre- and post-synaptic neurons as per spike-timing dependent plasticity (STDP).
- the STDP rule increases the conductance of a synapse if its post-synaptic neuron fires after its pre-synaptic neuron fires, and decreases the conductance of a synapse if the order of two firings is reversed. Further, the change depends on the precise delay between the two events: the more the delay, the less the magnitude of change.
- Artificial neural networks are computational systems that permit computers to essentially function in a manner analogous to that of biological brains. Artificial neural networks do not utilize the traditional digital model of manipulating 0s and Is. Instead, they create connections between processing elements, which are equivalent to neurons of a human brain. Artificial neural networks may be based on various electronic circuits that are modeled on neurons.
- phase change memory is described in Matthew J Breitwisch Ed - Akio Kawabata et al: "Phase Change Memory Interconnect Technology Conference, 2008. IITC 2008. International, IEEE, Piscataway, NJ, USA, 1 June 2008, pages 219-221, XP031274544ISBN: 978-1-4244-1911-1 .
- an apparatus comprising: a uni-polar, two-terminal bi-stable device connected to a pre-synaptic terminal; a diode having first and second ends, the diode connected at the first end to said bi-stable device and connected at the second end to a post-synaptic terminal; and at least one pulse shaper element generating a series of voltage pulses to said pre-synaptic and post-synaptic terminals, wherein in response to a received pre-synaptic spike, said pulse shaper element generates a pulse at said pre-synaptic terminal that occurs a predetermined period of time after said received pre-synaptic spike, and wherein in response to a post-synaptic spike, said pulse shaper element generates a pulse at said post-synaptic terminal that starts at a baseline value and reaches a first voltage value a first period of time after said post-synaptic spike, followed
- a computer program product for performing the aforementioned method.
- Embodiments of the invention provide a system, method and computer readable medium for electronic learning synapses with spike-dependent plasticity using memory switching elements.
- the term "neuronā was coined by Heinrich Wilhelm Gottfried von Waldeyer-Hartz in 1891 to capture the discrete information processing units of the brain.
- the junctions between two neurons were termed āsynapsesā by Sir Charles Sherrington in 1897. Information flows only along one direction through a synapse, thus we talk about a "pre-synapticā and a "post-synapticā neuron.
- Neurons when activated by sufficient input received via synapses, emit āspikesā that are delivered to those synapses that the neuron is pre-synaptic to. Neurons can be either āexcitatoryā or āinhibitory.ā
- a brain can be thought of as a directed graph where nodes are neurons and edges are synapses.
- the following table shows the rough number of neurons and synapses in a mouse, rat, and human. Each neuron in mammalian cortex makes roughly 8,000 synapses with other neurons.
- the computation, communication, and memory resources of the brain all scale with the number of synapses and not with the number of neurons. Even power and space requirements scale as number of synapses.
- synapses Some of the physical characteristics of the synapses are as follows. Synaptic density is roughly 7.2 x 10 8 per mm 3 which roughly corresponds to placing synapses at a three dimensional grid with 1 ā m spacing in every direction. This figure seems to be a constant of nature across all mammalian cortices.
- Synaptic weight is the influence that a pre-synaptic firing will have on post-synaptic neuron.
- Synaptic weights are plastic or adaptive, and change through time. Synaptic weight exhibits two forms of plasticity: (a) Long-term and (b) Short-term. Long-term changes in the transmission properties of synapses provide a physiological substrate for learning and memory, whereas short-term changes support a variety of computations. The mechanism of short-term plasticity is a form of gain control, and is not treated in this disclosure. The mechanism of long-term weight adaptation is known as spike-timing dependent plasticity (STDP).
- STDP spike-timing dependent plasticity
- Causality is a key element of STDP. Correlated activity can occur purely by chance, rather than reflecting a causal relationship that should be learned.
- STDP can be summarized as follows: if the post-synaptic neuron fires within a short time of the pre-synaptic neuron, then synapse is turned fully ON, whereas if the pre-synaptic neuron fires within a short time of the post-synaptic neuron, then synapse is turned fully OFF.
- the STDP rule permits the brain to extract causality and correlations from a spatio-temporally varying environment.
- a key characteristic of classical von Neumann computing is the separation of computation and memory. Specifically, if a memory location is to be modified, it is brought into a separate computing unit, modified, and then restored. This three-step process creates the classical von Neumann bottleneck that has plagued modern computer systems. In contrast to von Neumann computing, synapses are memory elements that are modified in-place, that is, memory and computation are distributed in the brain.
- embodiments of the present invention include a device that exhibits synapse-like function.
- embodiments make use of a memory-switching element whose program and erase operations can be accomplished with the same voltage polarity.
- a memory-switching element whose program and erase operations can be accomplished with the same voltage polarity.
- An example of such a device is a phase-change memory (PCM), which in this case is a uni-polar, two-terminal, bi-stable device.
- PCM phase-change memory
- a phase-change memory device can be switched as follows: a lower voltage (current) pulse to program or set (that is, go from low conductance amorphous state to high conductance crystalline state) and a higher voltage (current) pulse to erase or reset (that is, go from high conductance to low conductance state).
- Embodiments of the invention make use of novel bipolar pre and post-synaptic pulses that can capture the essence of STDP in such materials. Specifically, by using the PCM device in series with a diode, these novel pre and post-synaptic pulses can be shaped to program the device if the post-synaptic pulse follows the pre-synaptic pulse within 100 ms, or erase the device if the pre-synaptic pulse follows the post-synaptic pulse within 100 ms. While the disclosed embodiments may not permit multiple conductance states, they can reward causality and punish anti-causality in a STDP-like way. Embodiments of the present invention disclose a synapse-like device, which breaks the mold of traditional computing by creating a form of active memory.
- FIG. 1 shows a schematic of the artificial synapse system 10 which consists of an artificial binary synapse 12, and a two terminal PCM device 14 in series with a diode 16.
- the artificial binary synapse 12 includes a pre-synaptic node 18 and a post-synaptic node 20.
- a pulse shaper unit 22 has a pre-synaptic output 24 connected to the pre-synaptic node 18 and a post-synaptic output connected to the post-synaptic node 20.
- the pulse shaper unit 22 includes a pre-synaptic spike input 24 and a post-synaptic spike input 26.
- the pulse shaper unit 22 receives pre-synaptic and post-synaptic spikes at its two inputs 24, 26 respectively, and transforms them into pre-synaptic and post-synaptic input pulses as described below and shown in FIGs. 2-6 .
- separate electronic devices or components may be employed in the place of the single pulse shaper unit 22: one receiving the pre-synaptic spike and one receiving the post-synaptic spike.
- RESET high conductance crystalline
- the diode 16 is assumed to have the following property. Voltages ā 0.7V applied between the anode and cathode will not result in any current being passed. Voltages > 0.7V will result in the diode turning on with low resistance. A negligible amount of voltage is assumed to drop across the diode in its turned-on state. Thus the diode serves as a voltage level shifter for all voltages > 0.7 V.
- the pulse shaper unit 22 transforms the raw pre-synaptic and post-synaptic spikes received at its inputs 24, 26 into specially shaped pulses that are shown in FIG. 2 . In some embodiments, the pulse shaper unit 22 could be shared among many synapses similar to synapse 12 in order to reduce overall area consumption.
- the pulse shaper unit 22 generates the pre-synaptic pulse shown in FIG. 2 , which is essentially a 1.5V spike of very narrow width (e.g., 10-100 nsec) and is triggered 100 ms after the arrival of the original pre-synaptic spike received at its input 24.
- the pulse shaper unit 22 also generates a post-synaptic pulse which has two parts: a -0.3V level voltage triggered a few ms (e.g., 5 ms) after the arrival of the original post-synaptic spike at post-synaptic input 26 followed by a -0.6V level voltage 100 ms after the arrival of the original post-synaptic spike.
- This -0.6V level voltage relaxes back to 0V in a time slightly shorter than 100 ms (95 ms in this example).
- zero (0) V is applied to both the pre-synaptic and post-synaptic nodes 18, 20 of the artificial binary synapse 12 shown in FIG. 1 .
- the effective voltages that develop across the artificial binary synapse 12 and the PCM device 14 are shown in FIG. 3 .
- the main purpose of the diode 16 is apparent. By rectifying the current that could potentially flow through the synapse, the energy dissipated per synaptic operation is greatly reduced since current flows for 10-100 ns, instead of ā 200 ms.
- FIG. 7 is a flowchart of a process 30 for causing an artificial synapse, such as the artificial synapse 12, to exhibit STDP-like behavior in accordance with an embodiment of the invention.
- a pre-synaptic spike is received at input 24.
- a post-synaptic spike is received at input 26.
- a pre-synaptic pulse is generated by the pulse shaper unit 22 a predetermined period of time after the pre-synaptic spike, in block 36.
- a post-synaptic pulse is generated by the pulse shaper unit 22 having multiple voltage levels at specified times, in block 38. In particular, this may comprise the post-synaptic pulse shown in FIG. 2 .
- the generated pre-synaptic pulse is applied to a pre-synaptic node of a synaptic device, such as device 12.
- the generated post-synaptic pulse is applied to a post-synaptic node of a synaptic device, such as device 12.
- the full width half maximum (FWHM) of the one of the voltage pulses is shorter in duration than the FWHM of the other of the voltage pulses by at least a factor of 1000.
- the shapes of the pre-synaptic and post-synaptic pulses may be switched and the resulting pre-synaptic pulse may be reflected around the y-axis.
- a pre-synaptic pulse in response to the pre-synaptic spike, is generated that starts at a baseline level and reaches a first voltage level a first period of time after the pre-synaptic spike, followed by a second voltage level a second period of time after the pre synaptic spike, followed by a return to the baseline voltage a third period of time after the pre-synaptic spike.
- a post-synaptic pulse is generated that occurs a predetermined period of time after the received post-synaptic spike.
- devices which are not uni-polar may be used; however, the pulses may need to be modified.
- devices other than two-terminal bi-stable PCM devices may be used.
- devices other than PCM devices may be used, which have the property that their resistance can be changed as a function of the voltage applied across it, or as a function of the current running through it.
- the present invention may be used in a variety of architectures for various purposes, such as to form spatio-temporal associations between a neural network and environmental events.
- One such example may be to embed the present invention in a cross bar array that forms a set of synapses connected to a set of neurons in an artificial neural network.
- the artificial synapse 10 in FIG. 1 may be connected at the junction of the vertical and horizontal bars of the cross-bar array.
- the vertical bars may be pre-synaptic wires connected to the pre-synaptic input 24 and the horizontal bars may be post-synaptic wires connected to the post-synaptic input 26 shown in FIG. 1 .
- embodiments of the invention provide an electronic learning synapse with STDP plasticity using memory-switching elements.
- the present invention may be embodied as a system, method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit,ā "moduleā or "system.ā
- the present invention may take the form of a computer program product embodied in any tangible medium of expression having computer usable program code embodied in the medium. Any combination of one or more computer usable or computer readable medium(s) may be utilized.
- the computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.
- the computer-readable medium include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CDROM), an optical storage device, a transmission media such as those supporting the Internet or an intranet, or a magnetic storage device.
- the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for instance, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
- a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
- the computer-usable medium may include a propagated data signal with the computer-usable program code embodied therewith, either in baseband or as part of a carrier wave.
- the computer usable program code may be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, and/or RF, etc.
- Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
- the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
- the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
- LAN local area network
- WAN wide area network
- Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
- These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
- the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
- each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
- the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
- FIG. 8 is a high level block diagram showing an information processing system useful for implementing one embodiment of the present invention.
- the computer system includes one or more processors, such as processor 102.
- the processor 102 is connected to a communication infrastructure 104 (e.g., a communications bus, cross-over bar, or network).
- a communication infrastructure 104 e.g., a communications bus, cross-over bar, or network.
- the computer system can include a display interface 106 that forwards graphics, text, and other data from the communication infrastructure 104 (or from a frame buffer not shown) for display on a display unit 108.
- the computer system also includes a main memory 110, preferably random access memory (RAM), and may also include a secondary memory 112.
- the secondary memory 112 may include, for example, a hard disk drive 114 and/or a removable storage drive 116, representing, for example, a floppy disk drive, a magnetic tape drive, or an optical disk drive.
- the removable storage drive 116 reads from and/or writes to a removable storage unit 118 in a manner well known to those having ordinary skill in the art.
- Removable storage unit 118 represents, for example, a floppy disk, a compact disc, a magnetic tape, or an optical disk, etc. which is read by and written to by removable storage drive 116.
- the removable storage unit 118 includes a computer readable medium having stored therein computer software and/or data.
- the secondary memory 112 may include other similar means for allowing computer programs or other instructions to be loaded into the computer system.
- Such means may include a removable storage unit 120 and an interface 122.
- Other examples of such means may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 120 and interfaces 122 which allow software and data to be transferred from the removable storage unit 120 to the computer system.
- the computer system may also include a communications interface 124.
- Communications interface 124 allows software and data to be transferred between the computer system and external devices. Examples of communications interface 124 may include a modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card, etc.
- Software and data transferred via communications interface 124 are in the form of signals which may be, for example, electronic, electromagnetic, optical, or other signals capable of being received by communications interface 124. These signals are provided to communications interface 124 via a communications path (i.e., channel) 126.
- This communications path 126 carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, and/or other communications channels.
- computer program medium ācomputer usable medium,ā and ācomputer readable mediumā are used to generally refer to media such as main memory 110 and secondary memory 112, removable storage drive 116, and a hard disk installed in hard disk drive 114.
- Computer programs are stored in main memory 110 and/or secondary memory 112. Computer programs may also be received via communications interface 124. Such computer programs, when executed, enable the computer system to perform the features of the present invention as discussed herein. In particular, the computer programs, when executed, enable the processor 102 to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.
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Description
- The present invention relates to artificial neural networks, and more specifically, to electronic learning synapses with spike-dependent plasticity.
- The point of contact between an axon of a neuron and a dendrite on another neuron is called a synapse, and with respect to the synapse, the two neurons are respectively called pre-synaptic and post-synaptic. The essence of our individual experiences is stored in conductance of the synapses. The synaptic conductance changes with time as a function of the relative spike times of pre- and post-synaptic neurons as per spike-timing dependent plasticity (STDP). The STDP rule increases the conductance of a synapse if its post-synaptic neuron fires after its pre-synaptic neuron fires, and decreases the conductance of a synapse if the order of two firings is reversed. Further, the change depends on the precise delay between the two events: the more the delay, the less the magnitude of change.
- Artificial neural networks are computational systems that permit computers to essentially function in a manner analogous to that of biological brains. Artificial neural networks do not utilize the traditional digital model of manipulating 0s and Is. Instead, they create connections between processing elements, which are equivalent to neurons of a human brain. Artificial neural networks may be based on various electronic circuits that are modeled on neurons.
- A study of synapses is provided in BernabƩ Linares-Barranco and Teresa Serrano-Gotarredona: "Memristance can explain Spike-Time-Dependent-Plasticity in Neural Synapses" Nature Precedings 31 March 2009, XP00259010149 DOI: 10101/npre.2009.3010.1; Retrieved from the Internet: URL: http://precedings.nature.com/documents/3010/version/1/files/npre20093010-1.pdf.
- A conventional phase change memory is described in Matthew J Breitwisch Ed - Akio Kawabata et al: "Phase Change Memory Interconnect Technology Conference, 2008. IITC 2008. International, IEEE, Piscataway, NJ, USA, 1 June 2008, pages 219-221, XP031274544ISBN: 978-1-4244-1911-1.
- According to one embodiment of the present invention there is provided an apparatus comprising: a uni-polar, two-terminal bi-stable device connected to a pre-synaptic terminal; a diode having first and second ends, the diode connected at the first end to said bi-stable device and connected at the second end to a post-synaptic terminal; and at least one pulse shaper element generating a series of voltage pulses to said pre-synaptic and post-synaptic terminals, wherein in response to a received pre-synaptic spike, said pulse shaper element generates a pulse at said pre-synaptic terminal that occurs a predetermined period of time after said received pre-synaptic spike, and wherein in response to a post-synaptic spike, said pulse shaper element generates a pulse at said post-synaptic terminal that starts at a baseline value and reaches a first voltage value a first period of time after said post-synaptic spike, followed by a second voltage value a second period of time after said post synaptic spike, followed by a return to said baseline voltage a third period of time after said post-synaptic spike; wherein said pulse shaper element is further configured for:
- a) applying a first voltage pulse to a first terminal of said bi-stable device, said device including a resistive memory element;
- b) applying a second voltage pulse to a second terminal of said bi-stable device,
wherein said two voltage pulses have amplitudes and temporal profiles that are selected such that, depending on the relative arrival times of said first and second voltage spikes, a FIRST one of the following three effective voltages V1, V2, V3 is formed across said device:- i) V1, which acts to read the resistance of said device without changing the state of said device, V1 being obtained in the event there is substantially no temporal overlap between said first and second voltage pulses;
- ii) V2, which places said device in a lower-resistance state, V2 being obtained in the event said first voltage pulse begins before said second voltage pulse, and there is substantial temporal overlap between said first and second voltage pulses; and
- iii) V3, which places said device in a higher-resistance state, V3 being obtained in the event said second voltage pulse begins before said first voltage pulse, and there is substantial temporal overlap between said first and second voltage pulses;
- c) repeating a) to obtain a SECOND one of V1, V2, V3; and
- d) repeating a) to obtain a THIRD one of V1, V2, V3.
- According to another embodiment of the present invention there is provided a method comprising the steps of:
- a) providing a uni-polar, two terminal bistable device connected to a pre-synaptic terminal;
- b) providing a diode having a first end connected to the bistable device and a second end connected to a post-synaptic terminal;
- c) receiving a pre-synaptic spike at a pulse shaper element;
in response to receipt of the pre-synaptic spike, generating by the pulse shaper element a voltage pulse at the pre-synaptic terminal that occurs a predetermined period of time after the received pre-synaptic spike; - d) receiving a post-synaptic spike at the pulse shaper element;
- e) in response to receipt of the post-synaptic spike, generating by the pulse shaper element a voltage pulse at the post-synaptic terminal that starts at a baseline value and reaches a first voltage value a first period of time after said post-synaptic spike, followed by a second voltage value a second period of time after said post-synaptic spike, followed by a return to said baseline voltage a third period of time after said post-synaptic spike;
- f) applying a first voltage pulse to a first terminal of said bi-stable device, said device including a resistive memory element;
- g) applying a second voltage pulse to a second terminal of said bi-stable device, wherein said two voltage pulses have amplitudes and temporal profiles that are selected such that, depending on the relative arrival times of said first and second voltage spikes, a FIRST one of the following three effective voltages V1, V2, V3 is formed across said device:
- i) V1, which acts to read the resistance of said device without changing the state of said device, V1 being obtained in the event there is substantially no temporal overlap between said first and second voltage pulses;
- ii) V2, which places said device in a lower-resistance state, V2 being obtained in the event said first voltage pulse begins before said second voltage pulse, and there is substantial temporal overlap between said first and second voltage pulses; and
- iii) V3, which places said device in a higher-resistance state, V3 being obtained in the event said second voltage pulse begins before said first voltage pulse, and there is substantial temporal overlap between said first and second voltage pulses;
- h) repeating g) to obtain a SECOND one of V1, V2, V3; and
- i) repeating g) to obtain a THIRD one of V1, V2, V3.
- According to another embodiment of the present invention, a computer program product for performing the aforementioned method.
- Preferred embodiment of the present invention will now, by way of example only, with reference to the accompanying drawings, in which:
-
FIG. 1 shows a block diagram of an artificial synapse system in accordance with an embodiment of the invention; -
FIG. 2 shows exemplary input pulses used with the artificial synapse system in accordance with an embodiment of the invention; -
FIG. 3 shows exemplary effective pulses resulting from the input pulses shown inFIG. 2 in accordance with an embodiment of the invention; -
FIG. 4 shows exemplary input and effective pulses in accordance with an embodiment of the invention; -
FIG. 5 shows exemplary input and effective pulses in accordance with an embodiment of the invention; -
FIG. 6 shows exemplary input and effective pulses in accordance with an embodiment of the invention; -
FIG. 7 shows a flowchart of a process for producing spike-timing dependent plasticity in an artificial synapse in accordance with an embodiment of the invention; and -
FIG. 8 shows a high level block diagram of an information processing system useful for implementing one embodiment of the present invention. - Embodiments of the invention provide a system, method and computer readable medium for electronic learning synapses with spike-dependent plasticity using memory switching elements. The term "neuron" was coined by Heinrich Wilhelm Gottfried von Waldeyer-Hartz in 1891 to capture the discrete information processing units of the brain. The junctions between two neurons were termed "synapses" by Sir Charles Sherrington in 1897. Information flows only along one direction through a synapse, thus we talk about a "pre-synaptic" and a "post-synaptic" neuron. Neurons, when activated by sufficient input received via synapses, emit "spikes" that are delivered to those synapses that the neuron is pre-synaptic to. Neurons can be either "excitatory" or "inhibitory."
- A brain can be thought of as a directed graph where nodes are neurons and edges are synapses. The following table shows the rough number of neurons and synapses in a mouse, rat, and human. Each neuron in mammalian cortex makes roughly 8,000 synapses with other neurons.
Mouse Rat Human Neurons 16 Ć 106 56 Ć 106 22 Ć 109 Synapses 128 Ć 109 448 Ć 109 220 Ć 1012 - The computation, communication, and memory resources of the brain all scale with the number of synapses and not with the number of neurons. Even power and space requirements scale as number of synapses.
- Some of the physical characteristics of the synapses are as follows. Synaptic density is roughly 7.2 x 108 per mm3 which roughly corresponds to placing synapses at a three dimensional grid with 1 Āµ m spacing in every direction. This figure seems to be a constant of nature across all mammalian cortices.
- Synaptic weight is the influence that a pre-synaptic firing will have on post-synaptic neuron. Synaptic weights are plastic or adaptive, and change through time. Synaptic weight exhibits two forms of plasticity: (a) Long-term and (b) Short-term. Long-term changes in the transmission properties of synapses provide a physiological substrate for learning and memory, whereas short-term changes support a variety of computations. The mechanism of short-term plasticity is a form of gain control, and is not treated in this disclosure.
The mechanism of long-term weight adaptation is known as spike-timing dependent plasticity (STDP). Causality is a key element of STDP. Correlated activity can occur purely by chance, rather than reflecting a causal relationship that should be learned. Inputs that consistently are best at predicting a post-synaptic response should become the strongest inputs to the neuron. Thus in STDP, synapses are only strengthened if their pre-synaptic action potential precedes, and thus could have contributed to, the firing of the post synaptic neuron. Accidental, non-causal coincidences will weaken synapses. - We describe one of the prevalent phenomenological descriptions of STDP: (a) if pre-synaptic neuron fires t milliseconds before the post-synaptic neuron fires then the synaptic weight is increased (strengthened, potentiated) by A+ exp(- t/Ļ) where A+ and Ļ are constants; (b) if pre-synaptic neuron fires t milliseconds after the post-synaptic neuron fires then the synaptic weight is decreased (weakened, depressed) by A exp(- t/Ī») where A nd Ī» are constants.
- If the synapse is assumed to be binary, then, at the broadest level, STDP can be summarized as follows: if the post-synaptic neuron fires within a short time of the pre-synaptic neuron, then synapse is turned fully ON, whereas if the pre-synaptic neuron fires within a short time of the post-synaptic neuron, then synapse is turned fully OFF. The STDP rule permits the brain to extract causality and correlations from a spatio-temporally varying environment.
- A key characteristic of classical von Neumann computing is the separation of computation and memory. Specifically, if a memory location is to be modified, it is brought into a separate computing unit, modified, and then restored. This three-step process creates the classical von Neumann bottleneck that has plagued modern computer systems. In contrast to von Neumann computing, synapses are memory elements that are modified in-place, that is, memory and computation are distributed in the brain.
- In general, embodiments of the present invention include a device that exhibits synapse-like function. In particular, embodiments make use of a memory-switching element whose program and erase operations can be accomplished with the same voltage polarity. An example of such a device is a phase-change memory (PCM), which in this case is a uni-polar, two-terminal, bi-stable device. A phase-change memory device can be switched as follows: a lower voltage (current) pulse to program or set (that is, go from low conductance amorphous state to high conductance crystalline state) and a higher voltage (current) pulse to erase or reset (that is, go from high conductance to low conductance state). Embodiments of the invention make use of novel bipolar pre and post-synaptic pulses that can capture the essence of STDP in such materials. Specifically, by using the PCM device in series with a diode, these novel pre and post-synaptic pulses can be shaped to program the device if the post-synaptic pulse follows the pre-synaptic pulse within 100 ms, or erase the device if the pre-synaptic pulse follows the post-synaptic pulse within 100 ms. While the disclosed embodiments may not permit multiple conductance states, they can reward causality and punish anti-causality in a STDP-like way. Embodiments of the present invention disclose a synapse-like device, which breaks the mold of traditional computing by creating a form of active memory.
-
FIG. 1 shows a schematic of theartificial synapse system 10 which consists of an artificial binary synapse 12, and a two terminal PCM device 14 in series with adiode 16. The artificial binary synapse 12 includes apre-synaptic node 18 and apost-synaptic node 20. Apulse shaper unit 22 has a pre-synaptic output 24 connected to thepre-synaptic node 18 and a post-synaptic output connected to thepost-synaptic node 20. Thepulse shaper unit 22 includes a pre-synaptic spike input 24 and apost-synaptic spike input 26. Thepulse shaper unit 22 receives pre-synaptic and post-synaptic spikes at its twoinputs 24, 26 respectively, and transforms them into pre-synaptic and post-synaptic input pulses as described below and shown inFIGs. 2-6 . In some embodiments, separate electronic devices or components may be employed in the place of the single pulse shaper unit 22: one receiving the pre-synaptic spike and one receiving the post-synaptic spike. - The PCM device 14 is assumed to have a threshold voltage, VTh = 0.9V. If the PCM device 14 is in the low conductance amorphous (RESET) state, voltages less than VTh applied across it will not cause current to flow, but voltages greater than VTh will result in threshold switching behavior and current will flow through the device. Note that if the PCM device is in the high conductance crystalline (SET) state, then the threshold voltage criterion does not apply.
- In addition, we assume that a voltage pulse of magnitude approximately 1.1V will switch the PCM device 14 into the high conductance crystalline (SET) state regardless of the original state. Note that this voltage refers to the voltage across the PCM device. Finally, we assume that a voltage pulse of magnitude approximately 1.4V will switch the PCM device 14 into the low conductance amorphous (RESET) state regardless of the original state.
- The
diode 16 is assumed to have the following property. Voltages < 0.7V applied between the anode and cathode will not result in any current being passed. Voltages > 0.7V will result in the diode turning on with low resistance. A negligible amount of voltage is assumed to drop across the diode in its turned-on state. Thus the diode serves as a voltage level shifter for all voltages > 0.7 V. Thepulse shaper unit 22 transforms the raw pre-synaptic and post-synaptic spikes received at itsinputs 24, 26 into specially shaped pulses that are shown inFIG. 2 . In some embodiments, thepulse shaper unit 22 could be shared among many synapses similar to synapse 12 in order to reduce overall area consumption. - In an embodiment, the
pulse shaper unit 22 generates the pre-synaptic pulse shown inFIG. 2 , which is essentially a 1.5V spike of very narrow width (e.g., 10-100 nsec) and is triggered 100 ms after the arrival of the original pre-synaptic spike received at its input 24. Thepulse shaper unit 22 also generates a post-synaptic pulse which has two parts: a -0.3V level voltage triggered a few ms (e.g., 5 ms) after the arrival of the original post-synaptic spike atpost-synaptic input 26 followed by a -0.6V level voltage 100 ms after the arrival of the original post-synaptic spike. This -0.6V level voltage relaxes back to 0V in a time slightly shorter than 100 ms (95 ms in this example). When there are no pre-synaptic and post-synaptic spikes, atinputs 24, 26, zero (0) V is applied to both the pre-synaptic andpost-synaptic nodes FIG. 1 . - The effective voltages that develop across the artificial binary synapse 12 and the PCM device 14 are shown in
FIG. 3 . Here the main purpose of thediode 16 is apparent. By rectifying the current that could potentially flow through the synapse, the energy dissipated per synaptic operation is greatly reduced since current flows for 10-100 ns, instead of ā¼200 ms. - We now consider 3 cases:
- Case 1: Pre-synaptic pulse arrives just before post-synaptic pulse (within desired time window, 95ms in this example). As shown in
FIG. 4 , this results in an effective 1.1V pulse across the PCM device 14, which SETs the PCM device 14 (increases conductance). - Case 2: Pre-synaptic pulse arrives just after post-synaptic pulse (within desired time window, 95ms in this example). As shown in
FIG. 5 , the resulting 1.4V pulse across the PCM device 14, which RESETs the PCM device 14 (decreases conductance). - Case 3: Pre-synaptic pulse arrives much before/after post-synaptic pulse (outside of desired time window, 95ms in this example). As shown in
FIG. 6 , this results in a 0.8V pulse across the PCM device 14, which does not change the state of the PCM device 14. If originally RESET, 0.8V being less than VTh (0.9V), the PCM device 14 remains RESET. If originally SET, the pulse is passed through without RESETing the PCM device 14 (that would have required 1.4V), hence, the PCM device 14 remains SET. It will be appreciated that the precise values of actual voltages and time periods are for illustration only, and many other values may be used with different embodiments. -
FIG. 7 is a flowchart of aprocess 30 for causing an artificial synapse, such as the artificial synapse 12, to exhibit STDP-like behavior in accordance with an embodiment of the invention. Atblock 32, a pre-synaptic spike is received at input 24. At block 34 a post-synaptic spike is received atinput 26. A pre-synaptic pulse is generated by the pulse shaper unit 22 a predetermined period of time after the pre-synaptic spike, inblock 36. A post-synaptic pulse is generated by thepulse shaper unit 22 having multiple voltage levels at specified times, inblock 38. In particular, this may comprise the post-synaptic pulse shown inFIG. 2 . Inblock 40, the generated pre-synaptic pulse is applied to a pre-synaptic node of a synaptic device, such as device 12. Inblock 42, the generated post-synaptic pulse is applied to a post-synaptic node of a synaptic device, such as device 12. In one embodiment of the invention, the full width half maximum (FWHM) of the one of the voltage pulses is shorter in duration than the FWHM of the other of the voltage pulses by at least a factor of 1000. - In another embodiment of the invention, the shapes of the pre-synaptic and post-synaptic pulses may be switched and the resulting pre-synaptic pulse may be reflected around the y-axis. In this case, in response to the pre-synaptic spike, a pre-synaptic pulse is generated that starts at a baseline level and reaches a first voltage level a first period of time after the pre-synaptic spike, followed by a second voltage level a second period of time after the pre synaptic spike, followed by a return to the baseline voltage a third period of time after the pre-synaptic spike. Also, in response to the post-synaptic spike, a post-synaptic pulse is generated that occurs a predetermined period of time after the received post-synaptic spike. In other embodiments, devices which are not uni-polar may be used; however, the pulses may need to be modified. In other embodiments, devices other than two-terminal bi-stable PCM devices may be used. Also, devices other than PCM devices may be used, which have the property that their resistance can be changed as a function of the voltage applied across it, or as a function of the current running through it.
- The present invention may be used in a variety of architectures for various purposes, such as to form spatio-temporal associations between a neural network and environmental events. One such example may be to embed the present invention in a cross bar array that forms a set of synapses connected to a set of neurons in an artificial neural network. In such an arrangement, the
artificial synapse 10 inFIG. 1 may be connected at the junction of the vertical and horizontal bars of the cross-bar array. The vertical bars may be pre-synaptic wires connected to the pre-synaptic input 24 and the horizontal bars may be post-synaptic wires connected to thepost-synaptic input 26 shown inFIG. 1 . - As can be seen from the above disclosure, embodiments of the invention provide an electronic learning synapse with STDP plasticity using memory-switching elements. As will be appreciated by one skilled in the art, the present invention may be embodied as a system, method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," "module" or "system."
- Furthermore, the present invention, or components thereof, may take the form of a computer program product embodied in any tangible medium of expression having computer usable program code embodied in the medium. Any combination of one or more computer usable or computer readable medium(s) may be utilized. The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More examples of the computer-readable medium include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CDROM), an optical storage device, a transmission media such as those supporting the Internet or an intranet, or a magnetic storage device. Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for instance, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory. In the context of this document, a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-usable medium may include a propagated data signal with the computer-usable program code embodied therewith, either in baseband or as part of a carrier wave. The computer usable program code may be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, and/or RF, etc.
- Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
- The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
- These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
- The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
- The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
-
FIG. 8 is a high level block diagram showing an information processing system useful for implementing one embodiment of the present invention. The computer system includes one or more processors, such asprocessor 102. Theprocessor 102 is connected to a communication infrastructure 104 (e.g., a communications bus, cross-over bar, or network). Various software embodiments are described in terms of this exemplary computer system. After reading this description, it will become apparent to a person of ordinary skill in the relevant art(s) how to implement the invention using other computer systems and/or computer architectures. - The computer system can include a
display interface 106 that forwards graphics, text, and other data from the communication infrastructure 104 (or from a frame buffer not shown) for display on adisplay unit 108. The computer system also includes amain memory 110, preferably random access memory (RAM), and may also include asecondary memory 112. Thesecondary memory 112 may include, for example, ahard disk drive 114 and/or aremovable storage drive 116, representing, for example, a floppy disk drive, a magnetic tape drive, or an optical disk drive. Theremovable storage drive 116 reads from and/or writes to aremovable storage unit 118 in a manner well known to those having ordinary skill in the art.Removable storage unit 118 represents, for example, a floppy disk, a compact disc, a magnetic tape, or an optical disk, etc. which is read by and written to byremovable storage drive 116. As will be appreciated, theremovable storage unit 118 includes a computer readable medium having stored therein computer software and/or data. - In alternative embodiments, the
secondary memory 112 may include other similar means for allowing computer programs or other instructions to be loaded into the computer system. Such means may include aremovable storage unit 120 and aninterface 122. Other examples of such means may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and otherremovable storage units 120 andinterfaces 122 which allow software and data to be transferred from theremovable storage unit 120 to the computer system. - The computer system may also include a
communications interface 124. Communications interface 124 allows software and data to be transferred between the computer system and external devices. Examples ofcommunications interface 124 may include a modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card, etc. Software and data transferred viacommunications interface 124 are in the form of signals which may be, for example, electronic, electromagnetic, optical, or other signals capable of being received bycommunications interface 124. These signals are provided tocommunications interface 124 via a communications path (i.e., channel) 126. Thiscommunications path 126 carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, and/or other communications channels. - In this document, the terms "computer program medium," "computer usable medium," and "computer readable medium" are used to generally refer to media such as
main memory 110 andsecondary memory 112,removable storage drive 116, and a hard disk installed inhard disk drive 114. - Computer programs (also called computer control logic) are stored in
main memory 110 and/orsecondary memory 112. Computer programs may also be received viacommunications interface 124. Such computer programs, when executed, enable the computer system to perform the features of the present invention as discussed herein. In particular, the computer programs, when executed, enable theprocessor 102 to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system. - From the above description, it can be seen that the present invention provides a system, computer program product, and method for implementing the embodiments of the invention. References in the claims to an element in the singular is not intended to mean "one and only" unless explicitly so stated, but rather "one or more." All structural and functional equivalents to the elements of the above-described exemplary embodiment that are currently known or later come to be known to those of ordinary skill in the art are intended to be encompassed by the present claims.
- The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
- The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (11)
- An apparatus comprising:a uni-polar, two-terminal bi-stable device (14) connected to a pre-synaptic terminal (18);a diode (16) having first and second ends, the diode connected at the first end to said bi-stable device and connected at the second end to a post-synaptic terminal (20); andat least one pulse shaper element generating a series of voltage pulses to said pre-synaptic and post-synaptic terminals, wherein in response to a received pre-synaptic spike, said pulse shaper element generates a pulse at said pre-synaptic terminal that occurs a predetermined period of time after said received pre-synaptic spike, and wherein in response to a post-synaptic spike, said pulse shaper element generates a pulse at said post-synaptic terminal that starts at a baseline value and reaches a first voltage value a first period of time after said post-synaptic spike, followed by a second voltage value a second period of time after said post synaptic spike, followed by a return to said baseline voltage a third period of time after said post-synaptic spike;wherein said pulse shaper element is further configured for:a) applying a first voltage pulse to a first terminal of said bi-stable device, said device including a resistive memory element;b) applying a second voltage pulse to a second terminal of said bi-stable device,
wherein said two voltage pulses have amplitudes and temporal profiles that are selected such that, depending on the relative arrival times of said first and second voltage spikes, a FIRST one of the following three effective voltages V1, V2, V3 is formed across said device:i) V1, which acts to read the resistance of said device without changing the state of said device, V1 being obtained in the event there is substantially no temporal overlap between said first and second voltage pulses;ii) V2, which places said device in a lower-resistance state, V2 being obtained in the event said first voltage pulse begins before said second voltage pulse, and there is substantial temporal overlap between said first and second voltage pulses; andiii) V3, which places said device in a higher-resistance state, V3 being obtained in the event said second voltage pulse begins before said first voltage pulse, and there is substantial temporal overlap between said first and second voltage pulses;c) repeating a) to obtain a SECOND one of V1, V2, V3; andd) repeating a) to obtain a THIRD one of V1, V2, V3. - The apparatus according to Claim 1 wherein said bi-stable device changes from a first conductive state to a second conductive state based on the voltage of said generated pulse applied to its pre-synaptic and post-synaptic nodes, and also based on whether said pre-synaptic spike precedes or follows said post-synaptic spike within a predetermined window of time.
- The apparatus according to Claim 1, wherein the full width half maximum (FWHM) of one of said voltage pulses is shorter in duration than the FWHM of the other of said voltage pulses by at least a factor of 1000.
- The apparatus according to Claim 1, wherein the FWHM of one of said voltage pulses is no greater than 100 nanoseconds.
- The apparatus according to Claim 1, wherein said resistive memory element is a phase change memory element.
- The apparatus according to Claim 1 wherein said first and second voltage pulses have opposite polarities.
- The apparatus according to Claim 1 wherein V1, V2, V3 are of the same polarity.
- An apparatus according to claim 1 comprising:a plurality of uni-polar, two-terminal bi-stable devices each connected to the pre-synaptic terminal; anda plurality of rectifying elements having respective first and second ends, each connected at the first end to one of said bi-stable devices and connected at the second end to the post-synaptic terminal.
- The apparatus according to Claim 8 wherein said plurality of said devices are embedded within an artificial spiking neural network, and wherein said pulse shaper element repeatedly generates said series of voltage pulses to said pre-synaptic and post-synaptic terminals, thereby forming spatio-temporal associations between said neural network and environmental events.
- A method comprising the steps of:a) providing a uni-polar, two terminal bistable device (14) connected to a pre-synaptic terminal (18);b) providing a diode (16) having a first end connected to the bistable device and a second end connected to a post-synaptic terminal (20);c) receiving a pre-synaptic spike at a pulse shaper element (22);
in response to receipt of the pre-synaptic spike, generating by the pulse shaper element a voltage pulse at the pre-synaptic terminal that occurs a predetermined period of time after the received pre-synaptic spike;d) receiving a post-synaptic spike at the pulse shaper element;e) in response to receipt of the post-synaptic spike, generating by the pulse shaper element a voltage pulse at the post-synaptic terminal that starts at a baseline value and reaches a first voltage value a first period of time after said post-synaptic spike, followed by a second voltage value a second period of time after said post-synaptic spike, followed by a return to said baseline voltage a third period of time after said post-synaptic spike;f) applying a first voltage pulse to a first terminal of said bi-stable device, said device including a resistive memory element;g) applying a second voltage pulse to a second terminal of said bi-stable device, wherein said two voltage pulses have amplitudes and temporal profiles that are selected such that, depending on the relative arrival times of said first and second voltage spikes, a FIRST one of the following three effective voltages V1, V2, V3 is formed across said device:i) V1, which acts to read the resistance of said device without changing the state of said device, V1 being obtained in the event there is substantially no temporal overlap between said first and second voltage pulses;ii) V2, which places said device in a lower-resistance state, V2 being obtained in the event said first voltage pulse begins before said second voltage pulse, and there is substantial temporal overlap between said first and second voltage pulses; andiii) V3, which places said device in a higher-resistance state, V3 being obtained in the event said second voltage pulse begins before said first voltage pulse, and there is substantial temporal overlap between said first and second voltage pulses;h) repeating g) to obtain a SECOND one of V1, V2, V3; andi) repeating g) to obtain a THIRD one of V1, V2, V3. - A computer program product for providing pre-synaptic and post-synaptic pulses to a synaptic device, said computer program product comprising computer usable program code comprising instructions which, when executed by a processor of a data processing system, cause the data processing to perform the steps of the method claimed in claim 10.
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Families Citing this family (96)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8812418B2 (en) * | 2009-06-22 | 2014-08-19 | Hewlett-Packard Development Company, L.P. | Memristive adaptive resonance networks |
US9122994B2 (en) | 2010-03-26 | 2015-09-01 | Brain Corporation | Apparatus and methods for temporally proximate object recognition |
US9311593B2 (en) | 2010-03-26 | 2016-04-12 | Brain Corporation | Apparatus and methods for polychronous encoding and multiplexing in neuronal prosthetic devices |
US9405975B2 (en) | 2010-03-26 | 2016-08-02 | Brain Corporation | Apparatus and methods for pulse-code invariant object recognition |
US8467623B2 (en) | 2010-03-26 | 2013-06-18 | Brain Corporation | Invariant pulse latency coding systems and methods systems and methods |
US9129220B2 (en) * | 2010-07-07 | 2015-09-08 | Qualcomm Incorporated | Methods and systems for digital neural processing with discrete-level synapes and probabilistic STDP |
US9152915B1 (en) | 2010-08-26 | 2015-10-06 | Brain Corporation | Apparatus and methods for encoding vector into pulse-code output |
US8942466B2 (en) | 2010-08-26 | 2015-01-27 | Brain Corporation | Sensory input processing apparatus and methods |
US20120084240A1 (en) * | 2010-09-30 | 2012-04-05 | International Business Machines Corporation | Phase change memory synaptronic circuit for spiking computation, association and recall |
US9269042B2 (en) | 2010-09-30 | 2016-02-23 | International Business Machines Corporation | Producing spike-timing dependent plasticity in a neuromorphic network utilizing phase change synaptic devices |
JP5656202B2 (en) * | 2010-10-18 | 2015-01-21 | å½ē«å¤§å¦ę³äŗŗ大éŖå¤§å¦ | Feature extraction device, feature extraction method, and program thereof |
US8812414B2 (en) | 2011-05-31 | 2014-08-19 | International Business Machines Corporation | Low-power event-driven neural computing architecture in neural networks |
US9070039B2 (en) | 2013-02-01 | 2015-06-30 | Brian Corporation | Temporal winner takes all spiking neuron network sensory processing apparatus and methods |
US9147156B2 (en) | 2011-09-21 | 2015-09-29 | Qualcomm Technologies Inc. | Apparatus and methods for synaptic update in a pulse-coded network |
US9047568B1 (en) | 2012-09-20 | 2015-06-02 | Brain Corporation | Apparatus and methods for encoding of sensory data using artificial spiking neurons |
KR101888468B1 (en) | 2011-06-08 | 2018-08-16 | ģ¼ģ±ģ ģģ£¼ģķģ¬ | Synapse for a function cell of spike-timing-dependent plasticity(stdp), the function cell of spike-timing-dependent plasticity, and a neuromorphic circuit using the function cell of spike-timing-dependent plasticity |
FR2977351B1 (en) | 2011-06-30 | 2013-07-19 | Commissariat Energie Atomique | NON-SUPERVISING LEARNING METHOD IN ARTIFICIAL NEURON NETWORK BASED ON MEMORY NANO-DEVICES AND ARTIFICIAL NEURON NETWORK USING THE METHOD |
FR2977350B1 (en) | 2011-06-30 | 2013-07-19 | Commissariat Energie Atomique | NETWORK OF ARTIFICIAL NEURONS BASED ON COMPLEMENTARY MEMRISTIVE DEVICES |
US9053428B2 (en) | 2011-07-21 | 2015-06-09 | Qualcomm Incorporated | Method and apparatus of robust neural temporal coding, learning and cell recruitments for memory using oscillation |
US8843425B2 (en) * | 2011-07-29 | 2014-09-23 | International Business Machines Corporation | Hierarchical routing for two-way information flow and structural plasticity in neural networks |
US9147155B2 (en) | 2011-08-16 | 2015-09-29 | Qualcomm Incorporated | Method and apparatus for neural temporal coding, learning and recognition |
US9104973B2 (en) | 2011-09-21 | 2015-08-11 | Qualcomm Technologies Inc. | Elementary network description for neuromorphic systems with plurality of doublets wherein doublet events rules are executed in parallel |
US8719199B2 (en) | 2011-09-21 | 2014-05-06 | Brain Corporation | Systems and methods for providing a neural network having an elementary network description for efficient implementation of event-triggered plasticity rules |
US9460387B2 (en) * | 2011-09-21 | 2016-10-04 | Qualcomm Technologies Inc. | Apparatus and methods for implementing event-based updates in neuron networks |
US8725662B2 (en) | 2011-09-21 | 2014-05-13 | Brain Corporation | Apparatus and method for partial evaluation of synaptic updates based on system events |
US8725658B2 (en) | 2011-09-21 | 2014-05-13 | Brain Corporation | Elementary network description for efficient memory management in neuromorphic systems |
US8909576B2 (en) | 2011-09-16 | 2014-12-09 | International Business Machines Corporation | Neuromorphic event-driven neural computing architecture in a scalable neural network |
US9092735B2 (en) | 2011-09-21 | 2015-07-28 | Qualcomm Incorporated | Method and apparatus for structural delay plasticity in spiking neural networks |
US10210452B2 (en) | 2011-09-21 | 2019-02-19 | Qualcomm Incorporated | High level neuromorphic network description apparatus and methods |
US9098811B2 (en) | 2012-06-04 | 2015-08-04 | Brain Corporation | Spiking neuron network apparatus and methods |
US9117176B2 (en) | 2011-09-21 | 2015-08-25 | Qualcomm Technologies Inc. | Round-trip engineering apparatus and methods for neural networks |
US9111224B2 (en) * | 2011-10-19 | 2015-08-18 | Qualcomm Incorporated | Method and apparatus for neural learning of natural multi-spike trains in spiking neural networks |
US9111222B2 (en) * | 2011-11-09 | 2015-08-18 | Qualcomm Incorporated | Method and apparatus for switching the binary state of a location in memory in a probabilistic manner to store synaptic weights of a neural network |
US8996430B2 (en) | 2012-01-27 | 2015-03-31 | International Business Machines Corporation | Hierarchical scalable neuromorphic synaptronic system for synaptic and structural plasticity |
US9367797B2 (en) * | 2012-02-08 | 2016-06-14 | Jason Frank Hunzinger | Methods and apparatus for spiking neural computation |
US8909575B2 (en) | 2012-02-29 | 2014-12-09 | Qualcomm Incorporated | Method and apparatus for modeling neural resource based synaptic placticity |
US9224090B2 (en) * | 2012-05-07 | 2015-12-29 | Brain Corporation | Sensory input processing apparatus in a spiking neural network |
US9129221B2 (en) * | 2012-05-07 | 2015-09-08 | Brain Corporation | Spiking neural network feedback apparatus and methods |
US20130297539A1 (en) * | 2012-05-07 | 2013-11-07 | Filip Piekniewski | Spiking neural network object recognition apparatus and methods |
US9064215B2 (en) | 2012-06-14 | 2015-06-23 | Qualcomm Incorporated | Learning spike timing precision |
US8924322B2 (en) * | 2012-06-15 | 2014-12-30 | International Business Machines Corporation | Multi-processor cortical simulations with reciprocal connections with shared weights |
US9412041B1 (en) | 2012-06-29 | 2016-08-09 | Brain Corporation | Retinal apparatus and methods |
US9111215B2 (en) | 2012-07-03 | 2015-08-18 | Brain Corporation | Conditional plasticity spiking neuron network apparatus and methods |
US8977582B2 (en) | 2012-07-12 | 2015-03-10 | Brain Corporation | Spiking neuron network sensory processing apparatus and methods |
US9256823B2 (en) * | 2012-07-27 | 2016-02-09 | Qualcomm Technologies Inc. | Apparatus and methods for efficient updates in spiking neuron network |
US9159020B2 (en) | 2012-09-14 | 2015-10-13 | International Business Machines Corporation | Multiplexing physical neurons to optimize power and area |
US9311594B1 (en) | 2012-09-20 | 2016-04-12 | Brain Corporation | Spiking neuron network apparatus and methods for encoding of sensory data |
US9218563B2 (en) | 2012-10-25 | 2015-12-22 | Brain Corporation | Spiking neuron sensory processing apparatus and methods for saliency detection |
US9183493B2 (en) | 2012-10-25 | 2015-11-10 | Brain Corporation | Adaptive plasticity apparatus and methods for spiking neuron network |
US9111226B2 (en) | 2012-10-25 | 2015-08-18 | Brain Corporation | Modulated plasticity apparatus and methods for spiking neuron network |
US8943007B2 (en) | 2012-10-26 | 2015-01-27 | International Business Machines Corporation | Spike tagging for debugging, querying, and causal analysis |
US8990130B2 (en) * | 2012-11-21 | 2015-03-24 | International Business Machines Corporation | Consolidating multiple neurosynaptic cores into one memory |
US9275326B2 (en) | 2012-11-30 | 2016-03-01 | Brain Corporation | Rate stabilization through plasticity in spiking neuron network |
US9123127B2 (en) | 2012-12-10 | 2015-09-01 | Brain Corporation | Contrast enhancement spiking neuron network sensory processing apparatus and methods |
CN103078055B (en) * | 2013-01-04 | 2015-06-03 | åäøē§ęå¤§å¦ | Unit, device and method for simulating biological neuronal synapsis |
US9177245B2 (en) | 2013-02-08 | 2015-11-03 | Qualcomm Technologies Inc. | Spiking network apparatus and method with bimodal spike-timing dependent plasticity |
US9542643B2 (en) * | 2013-05-21 | 2017-01-10 | Qualcomm Incorporated | Efficient hardware implementation of spiking networks |
US9436909B2 (en) | 2013-06-19 | 2016-09-06 | Brain Corporation | Increased dynamic range artificial neuron network apparatus and methods |
US9239985B2 (en) | 2013-06-19 | 2016-01-19 | Brain Corporation | Apparatus and methods for processing inputs in an artificial neuron network |
US9552546B1 (en) | 2013-07-30 | 2017-01-24 | Brain Corporation | Apparatus and methods for efficacy balancing in a spiking neuron network |
US9558443B2 (en) | 2013-08-02 | 2017-01-31 | International Business Machines Corporation | Dual deterministic and stochastic neurosynaptic core circuit |
US9418332B2 (en) | 2013-08-16 | 2016-08-16 | Qualcomm Incorporated | Post ghost plasticity |
US9305256B2 (en) * | 2013-10-02 | 2016-04-05 | Qualcomm Incorporated | Automated method for modifying neural dynamics |
US9489623B1 (en) | 2013-10-15 | 2016-11-08 | Brain Corporation | Apparatus and methods for backward propagation of errors in a spiking neuron network |
US10339447B2 (en) * | 2014-01-23 | 2019-07-02 | Qualcomm Incorporated | Configuring sparse neuronal networks |
US9852006B2 (en) | 2014-03-28 | 2017-12-26 | International Business Machines Corporation | Consolidating multiple neurosynaptic core circuits into one reconfigurable memory block maintaining neuronal information for the core circuits |
US9939253B2 (en) | 2014-05-22 | 2018-04-10 | Brain Corporation | Apparatus and methods for distance estimation using multiple image sensors |
US10194163B2 (en) | 2014-05-22 | 2019-01-29 | Brain Corporation | Apparatus and methods for real time estimation of differential motion in live video |
US9713982B2 (en) | 2014-05-22 | 2017-07-25 | Brain Corporation | Apparatus and methods for robotic operation using video imagery |
US20150379397A1 (en) | 2014-06-28 | 2015-12-31 | Brainchip, Inc. | Secure voice signature communications system |
US9848112B2 (en) | 2014-07-01 | 2017-12-19 | Brain Corporation | Optical detection apparatus and methods |
US10057593B2 (en) | 2014-07-08 | 2018-08-21 | Brain Corporation | Apparatus and methods for distance estimation using stereo imagery |
US9870617B2 (en) | 2014-09-19 | 2018-01-16 | Brain Corporation | Apparatus and methods for saliency detection based on color occurrence analysis |
US9881349B1 (en) | 2014-10-24 | 2018-01-30 | Gopro, Inc. | Apparatus and methods for computerized object identification |
US10970625B2 (en) * | 2014-11-03 | 2021-04-06 | Hewlett Packard Enterprise Development Lp | Device with multiple resistance switches with different switching characteristics |
US9830981B2 (en) * | 2015-01-14 | 2017-11-28 | International Business Machines Corporation | Neuromorphic memory circuit using a leaky integrate and fire (LIF) line to transmit axon LIF pulse and a conductive denrite LIF line |
WO2016122472A1 (en) * | 2015-01-28 | 2016-08-04 | Hewlett Packard Enterprise Development Lp | Selector relaxation time reduction |
US10489705B2 (en) | 2015-01-30 | 2019-11-26 | International Business Machines Corporation | Discovering and using informative looping signals in a pulsed neural network having temporal encoders |
US9990580B2 (en) | 2015-03-13 | 2018-06-05 | International Business Machines Corporation | Neuromorphic synapses |
US10074050B2 (en) | 2015-07-13 | 2018-09-11 | Denso Corporation | Memristive neuromorphic circuit and method for training the memristive neuromorphic circuit |
US10332004B2 (en) | 2015-07-13 | 2019-06-25 | Denso Corporation | Memristive neuromorphic circuit and method for training the memristive neuromorphic circuit |
US10197664B2 (en) | 2015-07-20 | 2019-02-05 | Brain Corporation | Apparatus and methods for detection of objects using broadband signals |
US9767407B2 (en) | 2015-09-18 | 2017-09-19 | Samsung Electronics Co., Ltd. | Weighting device, neural network, and operating method of the weighting device |
CN107533459B (en) * | 2016-03-31 | 2020-11-20 | ę §äøåå±ęéč“£ä»»åä¼ä¼äø | Data processing method and unit using resistance memory array |
CN106292631A (en) * | 2016-08-25 | 2017-01-04 | åå°ę»Øēå·„å¤§å¦ | A kind of PWM rectifier fault diagnosis system based on neutral net |
CN106407988B (en) * | 2016-09-05 | 2019-11-26 | äøå½ē§å¦é¢čŖåØåē ē©¶ę | Cynapse detection method based on Electronic Speculum sequence section image |
CN110214330A (en) | 2016-10-27 | 2019-09-06 | ä½ē½éč¾¾å¤§å¦ē ē©¶åŗéä¼å ¬åø | The memristor of Neuromorphic circuit learns |
US20180146149A1 (en) | 2016-11-21 | 2018-05-24 | Samsung Electronics Co., Ltd. | Event-based sensor, user device including the same, and operation method of the same |
KR20180120511A (en) * | 2017-04-27 | 2018-11-06 | ģģ¤ģ¼ģ“ķģ“ėģ¤ ģ£¼ģķģ¬ | Neuromorphic Device Having Inverting Circuits |
JP6724870B2 (en) | 2017-06-19 | 2020-07-15 | ę Ŗå¼ä¼ē¤¾ćć³ć½ć¼ | Artificial neural network circuit training method, training program, and training device |
US11861429B2 (en) | 2018-04-30 | 2024-01-02 | Hewlett Packard Enterprise Development Lp | Resistive and digital processing cores |
US11593624B2 (en) | 2019-08-23 | 2023-02-28 | Micron Technology, Inc. | Self select memory cell based artificial synapse |
US11183238B2 (en) * | 2019-08-28 | 2021-11-23 | International Business Machines Corporation | Suppressing outlier drift coefficients while programming phase change memory synapses |
JP6899024B1 (en) * | 2020-06-11 | 2021-07-07 | ć¦ć£ć³ćć³ć ćØć¬ćÆććććÆć¹ ć³ć¼ćć¬ć¼ć·ć§ć³ | Resistance change type synapse array device |
US11557343B2 (en) | 2021-06-22 | 2023-01-17 | International Business Machines Corporation | Pulsing synaptic devices based on phase-change memory to increase the linearity in weight update |
US20220414453A1 (en) * | 2021-06-28 | 2022-12-29 | X Development Llc | Data augmentation using brain emulation neural networks |
Family Cites Families (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB9122362D0 (en) | 1991-10-22 | 1991-12-04 | British Telecomm | Resistive memory element |
US6141241A (en) | 1998-06-23 | 2000-10-31 | Energy Conversion Devices, Inc. | Universal memory element with systems employing same and apparatus and method for reading, writing and programming same |
US6314014B1 (en) | 1999-12-16 | 2001-11-06 | Ovonyx, Inc. | Programmable resistance memory arrays with reference cells |
US7092923B2 (en) | 2001-11-26 | 2006-08-15 | Exploitation Of Next Generation Co. Ltd. | Synapse element with learning function and semiconductor integrated circuit device including the synapse element |
US7412428B2 (en) | 2002-03-12 | 2008-08-12 | Knowmtech, Llc. | Application of hebbian and anti-hebbian learning to nanotechnology-based physical neural networks |
US6844582B2 (en) | 2002-05-10 | 2005-01-18 | Matsushita Electric Industrial Co., Ltd. | Semiconductor device and learning method thereof |
US6671710B2 (en) | 2002-05-10 | 2003-12-30 | Energy Conversion Devices, Inc. | Methods of computing with digital multistate phase change materials |
US6999953B2 (en) | 2002-07-03 | 2006-02-14 | Energy Conversion Devices, Inc. | Analog neurons and neurosynaptic networks |
US7186998B2 (en) | 2003-03-10 | 2007-03-06 | Energy Conversion Devices, Inc. | Multi-terminal device having logic functional |
US7085155B2 (en) | 2003-03-10 | 2006-08-01 | Energy Conversion Devices, Inc. | Secured phase-change devices |
US7426501B2 (en) | 2003-07-18 | 2008-09-16 | Knowntech, Llc | Nanotechnology neural network methods and systems |
KR100564602B1 (en) | 2003-12-30 | 2006-03-29 | ģ¼ģ±ģ ģģ£¼ģķģ¬ | Set programming method of phase-change memory array and writing driver circuit |
US7401058B2 (en) | 2004-04-29 | 2008-07-15 | University Of Massachusetts | Artificial neuron with phase-encoded logic |
JP4345676B2 (en) * | 2005-01-12 | 2009-10-14 | ćØć«ćć¼ćć”ć¢ćŖę Ŗå¼ä¼ē¤¾ | Semiconductor memory device |
JP4728055B2 (en) * | 2005-06-24 | 2011-07-20 | ćØć«ćć¼ćć”ć¢ćŖę Ŗå¼ä¼ē¤¾ | Artificial neural circuit |
KR100773095B1 (en) * | 2005-12-09 | 2007-11-02 | ģ¼ģ±ģ ģģ£¼ģķģ¬ | Phase change memory device and program method thereof |
US8138028B2 (en) | 2007-02-12 | 2012-03-20 | Macronix International Co., Ltd | Method for manufacturing a phase change memory device with pillar bottom electrode |
US20090029031A1 (en) | 2007-07-23 | 2009-01-29 | Tyler Lowrey | Methods for forming electrodes in phase change memory devices |
US7967994B2 (en) | 2007-10-25 | 2011-06-28 | Ovonyx, Inc. | Method and apparatus for chalcogenide device formation |
US8447714B2 (en) | 2009-05-21 | 2013-05-21 | International Business Machines Corporation | System for electronic learning synapse with spike-timing dependent plasticity using phase change memory |
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